Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations8075
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.2 MiB
Average record size in memory160.0 B

Variable types

Categorical3
Text1
Numeric16

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
CO3 is highly overall correlated with pHHigh correlation
Ca is highly overall correlated with Cl and 3 other fieldsHigh correlation
Cl is highly overall correlated with Ca and 5 other fieldsHigh correlation
EC is highly overall correlated with Ca and 8 other fieldsHigh correlation
HCO3 is highly overall correlated with Ca and 4 other fieldsHigh correlation
Mg is highly overall correlated with Cl and 4 other fieldsHigh correlation
Na is highly overall correlated with Cl and 6 other fieldsHigh correlation
QUALITY is highly overall correlated with ECHigh correlation
SO4 is highly overall correlated with Cl and 3 other fieldsHigh correlation
TDS is highly overall correlated with EC and 1 other fieldsHigh correlation
TH is highly overall correlated with Ca and 6 other fieldsHigh correlation
pH is highly overall correlated with CO3High correlation
CO3 has 6847 (84.8%) zeros Zeros
SO4 has 514 (6.4%) zeros Zeros
NO3 has 943 (11.7%) zeros Zeros
PO4 has 7073 (87.6%) zeros Zeros
K has 184 (2.3%) zeros Zeros
F has 665 (8.2%) zeros Zeros

Reproduction

Analysis started2024-11-05 06:02:47.015493
Analysis finished2024-11-05 06:03:06.569891
Duration19.55 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

STATE
Categorical

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.2 KiB
Madhya Pradesh
1112 
Chhattisgarh
844 
Uttar Pradesh
811 
Odisha
702 
TAMIL NADU
569 
Other values (21)
4037 

Length

Max length17
Median length13
Mean length10.086563
Min length5

Characters and Unicode

Total characters81449
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA&N Islands
2nd rowA&N Islands
3rd rowA&N Islands
4th rowA&N Islands
5th rowA&N Islands

Common Values

ValueCountFrequency (%)
Madhya Pradesh 1112
13.8%
Chhattisgarh 844
10.5%
Uttar Pradesh 811
10.0%
Odisha 702
 
8.7%
TAMIL NADU 569
 
7.0%
West Bengal 490
 
6.1%
RAJASTHAN 483
 
6.0%
Gujarat 438
 
5.4%
Kerala 327
 
4.0%
Haryana 313
 
3.9%
Other values (16) 1986
24.6%

Length

2024-11-05T11:33:06.629566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 2132
18.0%
madhya 1112
 
9.4%
chhattisgarh 844
 
7.1%
uttar 811
 
6.9%
odisha 702
 
5.9%
tamil 569
 
4.8%
nadu 569
 
4.8%
west 490
 
4.1%
bengal 490
 
4.1%
rajasthan 483
 
4.1%
Other values (22) 3626
30.7%

Most occurring characters

ValueCountFrequency (%)
a 13789
16.9%
h 7859
 
9.6%
r 6075
 
7.5%
s 5173
 
6.4%
t 4711
 
5.8%
d 4408
 
5.4%
3753
 
4.6%
e 3670
 
4.5%
A 2996
 
3.7%
P 2394
 
2.9%
Other values (30) 26621
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13789
16.9%
h 7859
 
9.6%
r 6075
 
7.5%
s 5173
 
6.4%
t 4711
 
5.8%
d 4408
 
5.4%
3753
 
4.6%
e 3670
 
4.5%
A 2996
 
3.7%
P 2394
 
2.9%
Other values (30) 26621
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13789
16.9%
h 7859
 
9.6%
r 6075
 
7.5%
s 5173
 
6.4%
t 4711
 
5.8%
d 4408
 
5.4%
3753
 
4.6%
e 3670
 
4.5%
A 2996
 
3.7%
P 2394
 
2.9%
Other values (30) 26621
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13789
16.9%
h 7859
 
9.6%
r 6075
 
7.5%
s 5173
 
6.4%
t 4711
 
5.8%
d 4408
 
5.4%
3753
 
4.6%
e 3670
 
4.5%
A 2996
 
3.7%
P 2394
 
2.9%
Other values (30) 26621
32.7%
Distinct476
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:06.790384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length8.144644
Min length3

Characters and Unicode

Total characters65768
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.3%

Sample

1st rowSouth Andaman
2nd rowSouth Andaman
3rd rowSouth Andaman
4th rowSouth Andaman
5th rowSouth Andaman
ValueCountFrequency (%)
south 177
 
2.0%
nagar 154
 
1.7%
raigarh 119
 
1.3%
24 115
 
1.3%
parganas 115
 
1.3%
andaman 90
 
1.0%
bilaspur 83
 
0.9%
jammu 71
 
0.8%
north 71
 
0.8%
jashpur 68
 
0.7%
Other values (465) 8007
88.3%
2024-11-05T11:33:07.055896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11629
17.7%
r 5496
 
8.4%
n 3636
 
5.5%
u 3538
 
5.4%
i 3448
 
5.2%
h 3190
 
4.9%
l 2201
 
3.3%
d 1985
 
3.0%
A 1885
 
2.9%
g 1834
 
2.8%
Other values (47) 26926
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11629
17.7%
r 5496
 
8.4%
n 3636
 
5.5%
u 3538
 
5.4%
i 3448
 
5.2%
h 3190
 
4.9%
l 2201
 
3.3%
d 1985
 
3.0%
A 1885
 
2.9%
g 1834
 
2.8%
Other values (47) 26926
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11629
17.7%
r 5496
 
8.4%
n 3636
 
5.5%
u 3538
 
5.4%
i 3448
 
5.2%
h 3190
 
4.9%
l 2201
 
3.3%
d 1985
 
3.0%
A 1885
 
2.9%
g 1834
 
2.8%
Other values (47) 26926
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11629
17.7%
r 5496
 
8.4%
n 3636
 
5.5%
u 3538
 
5.4%
i 3448
 
5.2%
h 3190
 
4.9%
l 2201
 
3.3%
d 1985
 
3.0%
A 1885
 
2.9%
g 1834
 
2.8%
Other values (47) 26926
40.9%

pH
Real number (ℝ)

High correlation 

Distinct328
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8363754
Minimum4.36
Maximum9.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:07.145794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.36
5-th percentile7.1
Q17.56
median7.84
Q38.12
95-th percentile8.62
Maximum9.73
Range5.37
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.46076908
Coefficient of variation (CV)0.058798749
Kurtosis2.8053798
Mean7.8363754
Median Absolute Deviation (MAD)0.28
Skewness-0.49827066
Sum63278.732
Variance0.21230814
MonotonicityNot monotonic
2024-11-05T11:33:07.240222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 273
 
3.4%
7.7 254
 
3.1%
7.9 219
 
2.7%
7.5 210
 
2.6%
7.6 205
 
2.5%
8 177
 
2.2%
7.4 170
 
2.1%
8.1 120
 
1.5%
8.2 98
 
1.2%
7.3 97
 
1.2%
Other values (318) 6252
77.4%
ValueCountFrequency (%)
4.36 1
< 0.1%
4.4 1
< 0.1%
4.54 1
< 0.1%
4.62 1
< 0.1%
4.69 1
< 0.1%
4.91 1
< 0.1%
5.1 1
< 0.1%
5.15 1
< 0.1%
5.27 1
< 0.1%
5.64 1
< 0.1%
ValueCountFrequency (%)
9.73 1
 
< 0.1%
9.25 1
 
< 0.1%
9.24 1
 
< 0.1%
9.23 1
 
< 0.1%
9.09 1
 
< 0.1%
9.08 1
 
< 0.1%
9.07 2
< 0.1%
9.05 3
< 0.1%
9.04 1
 
< 0.1%
9.03 1
 
< 0.1%

EC
Real number (ℝ)

High correlation 

Distinct2303
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean844.87133
Minimum0
Maximum5480
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:07.323527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151
Q1415.65
median665
Q31073.5
95-th percentile2190.6
Maximum5480
Range5480
Interquartile range (IQR)657.85

Descriptive statistics

Standard deviation648.41487
Coefficient of variation (CV)0.76747174
Kurtosis4.563005
Mean844.87133
Median Absolute Deviation (MAD)299
Skewness1.8462352
Sum6822336
Variance420441.85
MonotonicityNot monotonic
2024-11-05T11:33:07.410549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
650 37
 
0.5%
400 35
 
0.4%
450 34
 
0.4%
350 33
 
0.4%
550 31
 
0.4%
480 31
 
0.4%
500 31
 
0.4%
600 29
 
0.4%
380 27
 
0.3%
410 26
 
0.3%
Other values (2293) 7761
96.1%
ValueCountFrequency (%)
0 1
< 0.1%
17.74 1
< 0.1%
27.14 1
< 0.1%
28 1
< 0.1%
31 2
< 0.1%
31.33 1
< 0.1%
32 1
< 0.1%
34.5 1
< 0.1%
36 1
< 0.1%
37.44 1
< 0.1%
ValueCountFrequency (%)
5480 1
< 0.1%
5230 1
< 0.1%
4850 1
< 0.1%
4710 1
< 0.1%
4480 1
< 0.1%
4390 1
< 0.1%
4377 1
< 0.1%
4296 1
< 0.1%
4270 1
< 0.1%
4260 1
< 0.1%

CO3
Real number (ℝ)

High correlation  Zeros 

Distinct68
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1984838
Minimum0
Maximum104.16
Zeros6847
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:07.502498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile42
Maximum104.16
Range104.16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.224688
Coefficient of variation (CV)2.9286786
Kurtosis11.702425
Mean5.1984838
Median Absolute Deviation (MAD)0
Skewness3.3873328
Sum41977.757
Variance231.79113
MonotonicityNot monotonic
2024-11-05T11:33:07.600538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6847
84.8%
36 122
 
1.5%
24 105
 
1.3%
48 85
 
1.1%
7.057031581 82
 
1.0%
12 67
 
0.8%
60 56
 
0.7%
6 46
 
0.6%
13.02 41
 
0.5%
72 41
 
0.5%
Other values (58) 583
 
7.2%
ValueCountFrequency (%)
0 6847
84.8%
3 40
 
0.5%
4 1
 
< 0.1%
6 46
 
0.6%
7.057031581 82
 
1.0%
9 33
 
0.4%
10 1
 
< 0.1%
11.88 6
 
0.1%
12 67
 
0.8%
13.02 41
 
0.5%
ValueCountFrequency (%)
104.16 1
 
< 0.1%
96 17
0.2%
94 7
 
0.1%
91.14 1
 
< 0.1%
90 2
 
< 0.1%
84 22
0.3%
83 1
 
< 0.1%
82 4
 
< 0.1%
78.12 2
 
< 0.1%
78 24
0.3%

HCO3
Real number (ℝ)

High correlation 

Distinct861
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.36397
Minimum0
Maximum1464
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:07.690212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.73
Q1151.8
median244
Q3342
95-th percentile537
Maximum1464
Range1464
Interquartile range (IQR)190.2

Descriptive statistics

Standard deviation154.16266
Coefficient of variation (CV)0.59668793
Kurtosis2.7541401
Mean258.36397
Median Absolute Deviation (MAD)98
Skewness1.0738915
Sum2086289.1
Variance23766.127
MonotonicityNot monotonic
2024-11-05T11:33:07.783198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
244 188
 
2.3%
183 168
 
2.1%
220 165
 
2.0%
195 151
 
1.9%
268 149
 
1.8%
305 149
 
1.8%
207 149
 
1.8%
232 147
 
1.8%
256 146
 
1.8%
293 144
 
1.8%
Other values (851) 6519
80.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1.2 1
 
< 0.1%
2.5 2
 
< 0.1%
5 4
 
< 0.1%
5.1 6
 
0.1%
5.5 1
 
< 0.1%
6 17
0.2%
6.1 1
 
< 0.1%
6.42 1
 
< 0.1%
7.49 2
 
< 0.1%
ValueCountFrequency (%)
1464 1
< 0.1%
1366 1
< 0.1%
1293 1
< 0.1%
1257 1
< 0.1%
1121 1
< 0.1%
1110 1
< 0.1%
1098 1
< 0.1%
1074 1
< 0.1%
1061 1
< 0.1%
1049 1
< 0.1%

Cl
Real number (ℝ)

High correlation 

Distinct1014
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.680361
Minimum0
Maximum1156
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:07.873309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.37
Q121
median50
Q3121
95-th percentile362
Maximum1156
Range1156
Interquartile range (IQR)100

Descriptive statistics

Standard deviation131.9858
Coefficient of variation (CV)1.3240903
Kurtosis11.713023
Mean99.680361
Median Absolute Deviation (MAD)35
Skewness3.0205236
Sum804918.92
Variance17420.251
MonotonicityNot monotonic
2024-11-05T11:33:07.963454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 515
 
6.4%
21 411
 
5.1%
28 313
 
3.9%
35 221
 
2.7%
7 159
 
2.0%
50 157
 
1.9%
43 154
 
1.9%
25 142
 
1.8%
18 127
 
1.6%
57 118
 
1.5%
Other values (1004) 5758
71.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.78 1
< 0.1%
1.8 1
< 0.1%
2.3 1
< 0.1%
2.8 1
< 0.1%
3 1
< 0.1%
3.1 1
< 0.1%
3.2 1
< 0.1%
3.3 2
< 0.1%
3.5 1
< 0.1%
ValueCountFrequency (%)
1156 1
< 0.1%
1120 1
< 0.1%
1081 1
< 0.1%
1077 1
< 0.1%
1035 1
< 0.1%
1028 2
< 0.1%
1014 2
< 0.1%
978 2
< 0.1%
973 1
< 0.1%
971 1
< 0.1%

SO4
Real number (ℝ)

High correlation  Zeros 

Distinct1497
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.028895
Minimum0
Maximum547
Zeros514
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:08.051420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.295
median21.7
Q351
95-th percentile170
Maximum547
Range547
Interquartile range (IQR)42.705

Descriptive statistics

Standard deviation67.600635
Coefficient of variation (CV)1.5012724
Kurtosis14.999217
Mean45.028895
Median Absolute Deviation (MAD)16.7
Skewness3.412344
Sum363608.33
Variance4569.8459
MonotonicityNot monotonic
2024-11-05T11:33:08.137800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 514
 
6.4%
10 193
 
2.4%
5 165
 
2.0%
12 154
 
1.9%
8 133
 
1.6%
14 126
 
1.6%
15 122
 
1.5%
20 116
 
1.4%
22 109
 
1.3%
25 105
 
1.3%
Other values (1487) 6338
78.5%
ValueCountFrequency (%)
0 514
6.4%
0.02 1
 
< 0.1%
0.05 1
 
< 0.1%
0.1 2
 
< 0.1%
0.13 1
 
< 0.1%
0.15 1
 
< 0.1%
0.17 3
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
ValueCountFrequency (%)
547 1
< 0.1%
543 1
< 0.1%
542 1
< 0.1%
538 1
< 0.1%
537.65 1
< 0.1%
531 1
< 0.1%
530 1
< 0.1%
526 1
< 0.1%
522 1
< 0.1%
521 1
< 0.1%

NO3
Real number (ℝ)

Zeros 

Distinct1128
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.200423
Minimum0
Maximum264.3
Zeros943
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:08.228900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median12
Q333.1
95-th percentile89
Maximum264.3
Range264.3
Interquartile range (IQR)30.6

Descriptive statistics

Standard deviation32.631349
Coefficient of variation (CV)1.3483793
Kurtosis9.001596
Mean24.200423
Median Absolute Deviation (MAD)11.4
Skewness2.5902629
Sum195418.42
Variance1064.8049
MonotonicityNot monotonic
2024-11-05T11:33:08.325550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 943
 
11.7%
1 192
 
2.4%
2 154
 
1.9%
5 152
 
1.9%
10 141
 
1.7%
12 131
 
1.6%
11 117
 
1.4%
6 112
 
1.4%
3 112
 
1.4%
14 106
 
1.3%
Other values (1118) 5915
73.3%
ValueCountFrequency (%)
0 943
11.7%
0.01 4
 
< 0.1%
0.02 4
 
< 0.1%
0.03 4
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 5
 
0.1%
0.07 3
 
< 0.1%
0.08 4
 
< 0.1%
0.09 5
 
0.1%
ValueCountFrequency (%)
264.3 1
< 0.1%
263 1
< 0.1%
251 1
< 0.1%
250 2
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
237 1
< 0.1%
235 1
< 0.1%
232 1
< 0.1%
231 1
< 0.1%

PO4
Real number (ℝ)

Zeros 

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010596161
Minimum0
Maximum0.24
Zeros7073
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:08.548691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0965
Maximum0.24
Range0.24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.035095609
Coefficient of variation (CV)3.3121061
Kurtosis15.454218
Mean0.010596161
Median Absolute Deviation (MAD)0
Skewness3.8658684
Sum85.564
Variance0.0012317018
MonotonicityNot monotonic
2024-11-05T11:33:08.643871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 7073
87.6%
0.02 105
 
1.3%
0.1 101
 
1.3%
0.01 97
 
1.2%
0.05 94
 
1.2%
0.08 82
 
1.0%
0.2 64
 
0.8%
0.06 59
 
0.7%
0.12 58
 
0.7%
0.09 43
 
0.5%
Other values (32) 299
 
3.7%
ValueCountFrequency (%)
0 7073
87.6%
0.01 97
 
1.2%
0.011 1
 
< 0.1%
0.012 3
 
< 0.1%
0.015 1
 
< 0.1%
0.02 105
 
1.3%
0.021 1
 
< 0.1%
0.024 2
 
< 0.1%
0.025 2
 
< 0.1%
0.028 1
 
< 0.1%
ValueCountFrequency (%)
0.24 4
 
< 0.1%
0.23 5
 
0.1%
0.22 12
 
0.1%
0.21 18
 
0.2%
0.2 64
0.8%
0.19 4
 
< 0.1%
0.185 1
 
< 0.1%
0.182 1
 
< 0.1%
0.18 8
 
0.1%
0.17 9
 
0.1%

TH
Real number (ℝ)

High correlation 

Distinct972
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.38988
Minimum0
Maximum1600
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:08.733204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53.3
Q1140
median220
Q3320
95-th percentile590
Maximum1600
Range1600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation169.83459
Coefficient of variation (CV)0.66761535
Kurtosis4.9304698
Mean254.38988
Median Absolute Deviation (MAD)88
Skewness1.7359621
Sum2054198.3
Variance28843.787
MonotonicityNot monotonic
2024-11-05T11:33:08.827064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 192
 
2.4%
180 173
 
2.1%
210 159
 
2.0%
250 158
 
2.0%
170 157
 
1.9%
190 155
 
1.9%
160 154
 
1.9%
230 151
 
1.9%
150 151
 
1.9%
240 134
 
1.7%
Other values (962) 6491
80.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.1 1
 
< 0.1%
6 1
 
< 0.1%
6.4 1
 
< 0.1%
8.56 1
 
< 0.1%
8.9 1
 
< 0.1%
10 5
0.1%
10.7 1
 
< 0.1%
11.5 1
 
< 0.1%
12.5 1
 
< 0.1%
ValueCountFrequency (%)
1600 1
< 0.1%
1432 1
< 0.1%
1340 1
< 0.1%
1310 1
< 0.1%
1300 1
< 0.1%
1240 1
< 0.1%
1230 1
< 0.1%
1221 1
< 0.1%
1210 1
< 0.1%
1205 1
< 0.1%

Ca
Real number (ℝ)

High correlation 

Distinct603
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.51417
Minimum0
Maximum337
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:08.919933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.88
Q124
median42
Q368
95-th percentile124
Maximum337
Range337
Interquartile range (IQR)44

Descriptive statistics

Standard deviation38.458706
Coefficient of variation (CV)0.74656558
Kurtosis5.7154326
Mean51.51417
Median Absolute Deviation (MAD)20
Skewness1.8940488
Sum415976.92
Variance1479.0721
MonotonicityNot monotonic
2024-11-05T11:33:09.011443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 289
 
3.6%
36 280
 
3.5%
24 268
 
3.3%
32 265
 
3.3%
20 249
 
3.1%
48 242
 
3.0%
44 238
 
2.9%
28 232
 
2.9%
16 212
 
2.6%
52 204
 
2.5%
Other values (593) 5596
69.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
1.4 1
 
< 0.1%
1.71 1
 
< 0.1%
1.8 1
 
< 0.1%
2 10
0.1%
2.06 2
 
< 0.1%
2.1 4
 
< 0.1%
2.34 1
 
< 0.1%
2.7 1
 
< 0.1%
ValueCountFrequency (%)
337 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
297 1
< 0.1%
296 1
< 0.1%
284 2
< 0.1%
280 1
< 0.1%
274 1
< 0.1%
272 2
< 0.1%
270 1
< 0.1%

Mg
Real number (ℝ)

High correlation 

Distinct701
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.390176
Minimum-23
Maximum222
Zeros16
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size63.2 KiB
2024-11-05T11:33:09.104197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-23
5-th percentile3.9
Q112.465
median24
Q339
95-th percentile80
Maximum222
Range245
Interquartile range (IQR)26.535

Descriptive statistics

Standard deviation26.058017
Coefficient of variation (CV)0.8574487
Kurtosis7.7487842
Mean30.390176
Median Absolute Deviation (MAD)12.39
Skewness2.2109969
Sum245400.67
Variance679.02023
MonotonicityNot monotonic
2024-11-05T11:33:09.202619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 248
 
3.1%
22 244
 
3.0%
29 234
 
2.9%
24 231
 
2.9%
12 230
 
2.8%
19 213
 
2.6%
34 185
 
2.3%
15 183
 
2.3%
10 178
 
2.2%
36 173
 
2.1%
Other values (691) 5956
73.8%
ValueCountFrequency (%)
-23 1
 
< 0.1%
0 16
0.2%
0.036 1
 
< 0.1%
0.1 2
 
< 0.1%
0.14 1
 
< 0.1%
0.144 1
 
< 0.1%
0.338 1
 
< 0.1%
0.49 1
 
< 0.1%
0.58 1
 
< 0.1%
0.59 1
 
< 0.1%
ValueCountFrequency (%)
222 1
 
< 0.1%
219 1
 
< 0.1%
218 1
 
< 0.1%
216 1
 
< 0.1%
214 1
 
< 0.1%
208 3
< 0.1%
207 1
 
< 0.1%
202 1
 
< 0.1%
199 1
 
< 0.1%
192 1
 
< 0.1%

Na
Real number (ℝ)

High correlation 

Distinct1388
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.697707
Minimum0
Maximum763
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:09.296272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.2
Q120
median45
Q392
95-th percentile272
Maximum763
Range763
Interquartile range (IQR)72

Descriptive statistics

Standard deviation95.637121
Coefficient of variation (CV)1.2308873
Kurtosis9.6371994
Mean77.697707
Median Absolute Deviation (MAD)30
Skewness2.7486757
Sum627408.98
Variance9146.4589
MonotonicityNot monotonic
2024-11-05T11:33:09.395624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 112
 
1.4%
16 109
 
1.3%
18 96
 
1.2%
25 96
 
1.2%
17 96
 
1.2%
20 94
 
1.2%
10 93
 
1.2%
12 93
 
1.2%
19 92
 
1.1%
32 89
 
1.1%
Other values (1378) 7105
88.0%
ValueCountFrequency (%)
0 12
0.1%
0.16 1
 
< 0.1%
0.17 2
 
< 0.1%
0.22 1
 
< 0.1%
0.36 1
 
< 0.1%
0.38 2
 
< 0.1%
0.39 1
 
< 0.1%
0.41 1
 
< 0.1%
0.43 1
 
< 0.1%
0.86 1
 
< 0.1%
ValueCountFrequency (%)
763 1
< 0.1%
758 1
< 0.1%
750 1
< 0.1%
742 1
< 0.1%
730 2
< 0.1%
729 1
< 0.1%
722 1
< 0.1%
714 1
< 0.1%
708 2
< 0.1%
700 1
< 0.1%

K
Real number (ℝ)

Zeros 

Distinct1103
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6808923
Minimum0
Maximum68.87
Zeros184
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:09.496178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.63
Q11.8
median3.3
Q37
95-th percentile27
Maximum68.87
Range68.87
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation9.5758595
Coefficient of variation (CV)1.4333204
Kurtosis11.718523
Mean6.6808923
Median Absolute Deviation (MAD)2.1
Skewness3.1906511
Sum53948.206
Variance91.697084
MonotonicityNot monotonic
2024-11-05T11:33:09.624053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 516
 
6.4%
2 433
 
5.4%
3 267
 
3.3%
4 218
 
2.7%
0 184
 
2.3%
5 146
 
1.8%
6 124
 
1.5%
1.2 115
 
1.4%
2.1 105
 
1.3%
1.9 102
 
1.3%
Other values (1093) 5865
72.6%
ValueCountFrequency (%)
0 184
2.3%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.097 1
 
< 0.1%
0.1 2
 
< 0.1%
0.12 1
 
< 0.1%
0.13 2
 
< 0.1%
0.17 1
 
< 0.1%
0.18 1
 
< 0.1%
0.19 1
 
< 0.1%
ValueCountFrequency (%)
68.87 1
 
< 0.1%
68.76 1
 
< 0.1%
68.3 1
 
< 0.1%
68 2
< 0.1%
67.52 1
 
< 0.1%
67 2
< 0.1%
66.7 1
 
< 0.1%
66 3
< 0.1%
65 3
< 0.1%
64.88 1
 
< 0.1%

F
Real number (ℝ)

Zeros 

Distinct517
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48667878
Minimum0
Maximum3.9
Zeros665
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:09.813646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.17
median0.36
Q30.66
95-th percentile1.4
Maximum3.9
Range3.9
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.47978311
Coefficient of variation (CV)0.98583117
Kurtosis8.2495078
Mean0.48667878
Median Absolute Deviation (MAD)0.235
Skewness2.2851301
Sum3929.9312
Variance0.23019184
MonotonicityNot monotonic
2024-11-05T11:33:09.974698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 665
 
8.2%
0.1 249
 
3.1%
0.2 232
 
2.9%
0.3 170
 
2.1%
0.4 141
 
1.7%
0.05 107
 
1.3%
0.5 105
 
1.3%
0.24 98
 
1.2%
0.15 98
 
1.2%
0.6 97
 
1.2%
Other values (507) 6113
75.7%
ValueCountFrequency (%)
0 665
8.2%
0.01 25
 
0.3%
0.0156 1
 
< 0.1%
0.02 42
 
0.5%
0.0245 1
 
< 0.1%
0.03 46
 
0.6%
0.04 53
 
0.7%
0.0425 1
 
< 0.1%
0.0466 1
 
< 0.1%
0.05 107
 
1.3%
ValueCountFrequency (%)
3.9 3
< 0.1%
3.8 1
 
< 0.1%
3.68 1
 
< 0.1%
3.61 1
 
< 0.1%
3.6 1
 
< 0.1%
3.59 1
 
< 0.1%
3.55 1
 
< 0.1%
3.54 1
 
< 0.1%
3.52 1
 
< 0.1%
3.5 4
< 0.1%

SiO2
Real number (ℝ)

Distinct223
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.46864
Minimum0
Maximum82
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:10.074746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q124
median24
Q325
95-th percentile41
Maximum82
Range82
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.8831918
Coefficient of variation (CV)0.36304395
Kurtosis5.9987597
Mean24.46864
Median Absolute Deviation (MAD)1
Skewness1.38347
Sum197584.27
Variance78.911097
MonotonicityNot monotonic
2024-11-05T11:33:10.171170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 4016
49.7%
22 187
 
2.3%
28 171
 
2.1%
25 168
 
2.1%
29 158
 
2.0%
26 155
 
1.9%
27 152
 
1.9%
21 140
 
1.7%
23 140
 
1.7%
20 138
 
1.7%
Other values (213) 2650
32.8%
ValueCountFrequency (%)
0 3
< 0.1%
0.07 1
 
< 0.1%
0.37 1
 
< 0.1%
1 3
< 0.1%
1.07 1
 
< 0.1%
2 2
< 0.1%
2.26 1
 
< 0.1%
2.3 1
 
< 0.1%
2.43 1
 
< 0.1%
2.8 1
 
< 0.1%
ValueCountFrequency (%)
82 2
< 0.1%
80 2
< 0.1%
78 1
< 0.1%
77 2
< 0.1%
76 1
< 0.1%
75 2
< 0.1%
74.22 1
< 0.1%
74 2
< 0.1%
73 2
< 0.1%
72.4 1
< 0.1%

TDS
Real number (ℝ)

High correlation 

Distinct1751
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean545.49948
Minimum9.29
Maximum2301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-11-05T11:33:10.272331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.29
5-th percentile174.952
Q1491
median520
Q3520
95-th percentile1080
Maximum2301
Range2291.71
Interquartile range (IQR)29

Descriptive statistics

Standard deviation278.58411
Coefficient of variation (CV)0.51069547
Kurtosis9.1734993
Mean545.49948
Median Absolute Deviation (MAD)0
Skewness2.3611611
Sum4404908.3
Variance77609.109
MonotonicityNot monotonic
2024-11-05T11:33:10.375769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 4136
51.2%
527 14
 
0.2%
395 13
 
0.2%
403 12
 
0.1%
426 12
 
0.1%
549 11
 
0.1%
469 11
 
0.1%
293 11
 
0.1%
456 10
 
0.1%
455 10
 
0.1%
Other values (1741) 3835
47.5%
ValueCountFrequency (%)
9.29 1
< 0.1%
15.49 1
< 0.1%
17.78 1
< 0.1%
19.69 1
< 0.1%
21.31 1
< 0.1%
23.17 1
< 0.1%
24.31 1
< 0.1%
24.73 1
< 0.1%
24.83 1
< 0.1%
25.09 1
< 0.1%
ValueCountFrequency (%)
2301 2
< 0.1%
2275 1
< 0.1%
2254 1
< 0.1%
2253 1
< 0.1%
2252 1
< 0.1%
2243 2
< 0.1%
2225 1
< 0.1%
2224 1
< 0.1%
2222 1
< 0.1%
2221 1
< 0.1%

HARDNESS
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.2 KiB
Hard
6596 
Moderately
980 
Soft
 
499

Length

Max length10
Median length4
Mean length4.7281734
Min length4

Characters and Unicode

Total characters38180
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHard
2nd rowHard
3rd rowHard
4th rowHard
5th rowHard

Common Values

ValueCountFrequency (%)
Hard 6596
81.7%
Moderately 980
 
12.1%
Soft 499
 
6.2%

Length

2024-11-05T11:33:10.469245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T11:33:10.554421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hard 6596
81.7%
moderately 980
 
12.1%
soft 499
 
6.2%

Most occurring characters

ValueCountFrequency (%)
a 7576
19.8%
r 7576
19.8%
d 7576
19.8%
H 6596
17.3%
e 1960
 
5.1%
o 1479
 
3.9%
t 1479
 
3.9%
M 980
 
2.6%
l 980
 
2.6%
y 980
 
2.6%
Other values (2) 998
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7576
19.8%
r 7576
19.8%
d 7576
19.8%
H 6596
17.3%
e 1960
 
5.1%
o 1479
 
3.9%
t 1479
 
3.9%
M 980
 
2.6%
l 980
 
2.6%
y 980
 
2.6%
Other values (2) 998
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7576
19.8%
r 7576
19.8%
d 7576
19.8%
H 6596
17.3%
e 1960
 
5.1%
o 1479
 
3.9%
t 1479
 
3.9%
M 980
 
2.6%
l 980
 
2.6%
y 980
 
2.6%
Other values (2) 998
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7576
19.8%
r 7576
19.8%
d 7576
19.8%
H 6596
17.3%
e 1960
 
5.1%
o 1479
 
3.9%
t 1479
 
3.9%
M 980
 
2.6%
l 980
 
2.6%
y 980
 
2.6%
Other values (2) 998
 
2.6%

QUALITY
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.2 KiB
Safe
4325 
Moderately Safe
3386 
Unsafe
 
364

Length

Max length15
Median length4
Mean length8.7026625
Min length4

Characters and Unicode

Total characters70274
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSafe
2nd rowSafe
3rd rowSafe
4th rowSafe
5th rowModerately Safe

Common Values

ValueCountFrequency (%)
Safe 4325
53.6%
Moderately Safe 3386
41.9%
Unsafe 364
 
4.5%

Length

2024-11-05T11:33:10.635561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T11:33:10.710346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
safe 7711
67.3%
moderately 3386
29.5%
unsafe 364
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 14847
21.1%
a 11461
16.3%
f 8075
11.5%
S 7711
11.0%
M 3386
 
4.8%
o 3386
 
4.8%
d 3386
 
4.8%
r 3386
 
4.8%
t 3386
 
4.8%
l 3386
 
4.8%
Other values (5) 7864
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14847
21.1%
a 11461
16.3%
f 8075
11.5%
S 7711
11.0%
M 3386
 
4.8%
o 3386
 
4.8%
d 3386
 
4.8%
r 3386
 
4.8%
t 3386
 
4.8%
l 3386
 
4.8%
Other values (5) 7864
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14847
21.1%
a 11461
16.3%
f 8075
11.5%
S 7711
11.0%
M 3386
 
4.8%
o 3386
 
4.8%
d 3386
 
4.8%
r 3386
 
4.8%
t 3386
 
4.8%
l 3386
 
4.8%
Other values (5) 7864
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14847
21.1%
a 11461
16.3%
f 8075
11.5%
S 7711
11.0%
M 3386
 
4.8%
o 3386
 
4.8%
d 3386
 
4.8%
r 3386
 
4.8%
t 3386
 
4.8%
l 3386
 
4.8%
Other values (5) 7864
11.2%

Interactions

2024-11-05T11:33:05.028168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:47.684335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.026608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.105306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.247289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.419675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.548437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.777891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.873088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.967602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.254295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.401922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.450146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.592090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.834395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.976747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.089638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:47.762230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.100836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.164324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.310582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.486241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.613356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.841212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.934953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.035803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.319567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.460783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.520034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.657470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.900143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.037965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.153948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:47.829493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.165393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.227057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.377805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.554221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.680352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.907625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.000749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.109271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.388633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.528709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.588842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.724702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.969825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.102954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.224638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:47.901220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.230277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.287902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.455216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.619826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.747518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.973615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.077997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.186774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.456087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.591563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.656463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.792830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.037687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.166548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.292435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.012256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.299299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.355448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.525145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.693250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.819445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.056355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.148865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.259053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.533417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.661356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.734635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.996701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.112843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.238067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.357896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.089657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.365975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.522617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.593642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.760711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.889393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.123308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.214710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.329243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.635036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.725937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.803521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.067390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.190640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.305619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.423577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.156445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.432891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.584585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.659408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.827243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.953501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.188903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.279849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.399305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.701095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.787834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.870702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.137058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.261131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.368563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.487422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.220148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.497805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.650225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.724877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.892753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.028136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.252252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.343743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.470430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.768366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.851014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.939483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.208243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.328887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.433983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.554963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.289890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.562683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.715893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.788500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.961032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.221309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.316386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.410169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.539056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.835869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.914804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.008555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.275457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.399988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.496998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.624752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.359417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.632484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.785822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.858526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.046077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.292761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.389407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.497208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.614266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.910765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.984484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.086771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.350064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.474238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.566994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.729728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.475512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.697492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.850733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.926603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.127075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.363973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.463038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.567456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.686534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.981477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.051410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.166117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.421235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.550650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.636354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:05.930042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.581564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.757196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.910046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.995133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.192388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.429391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.526401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.629268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.757221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.048320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.115455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.233449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.486393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.617211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.695316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:06.000240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.704786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.825855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.987613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.079925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.266498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.504288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.597487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.702122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.835249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.124526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.187537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.306841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.559948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.693668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.768439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:06.069460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.811199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.891062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.057774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.173187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.338097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.574658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.670634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.771378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:57.909311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.197647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.254797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.381987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.630373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.766505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.835467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:06.141144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.891296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:49.982737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.127557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.252453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.414958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.650019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.746511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.842417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.122814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.271115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.326348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.456648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.705424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.843323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.907086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:06.204483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:48.960352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:50.043830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:51.187733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:52.340563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:53.483097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:54.712034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:55.809429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:56.905436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:58.189183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:32:59.335032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:00.389233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:01.525583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:02.770263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:03.908895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T11:33:04.967014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T11:33:10.781103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CO3CaClECFHARDNESSHCO3KMgNO3NaPO4QUALITYSO4STATESiO2TDSTHpH
CO31.000-0.230-0.0230.0510.0730.049-0.0180.0920.087-0.1130.140-0.0900.196-0.0410.264-0.1660.011-0.0640.577
Ca-0.2301.0000.5010.6080.1110.3920.5110.0530.3700.4140.3090.1910.3550.3960.1860.2420.3410.780-0.247
Cl-0.0230.5011.0000.8160.2640.1650.4560.2500.5820.4540.7740.1660.4150.5700.1610.1350.4660.663-0.062
EC0.0510.6080.8161.0000.3580.3500.7850.2090.7460.4670.8380.2060.5100.6290.2100.1840.6040.8390.024
F0.0730.1110.2640.3581.0000.1210.3560.0470.2790.0880.3970.0800.1680.2500.1460.1170.1580.2320.103
HARDNESS0.0490.3920.1650.3500.1211.0000.4480.0590.3790.1420.1490.0630.2150.1520.3920.1400.2770.4820.193
HCO3-0.0180.5110.4560.7850.3560.4481.0000.1230.6360.2190.6300.1810.4020.3240.1890.2310.4620.7130.031
K0.0920.0530.2500.2090.0470.0590.1231.0000.1380.0890.245-0.0360.1210.1810.110-0.0200.0870.1370.101
Mg0.0870.3700.5820.7460.2790.3790.6360.1381.0000.2820.5310.0730.4280.4840.1760.1180.4270.8100.069
NO3-0.1130.4140.4540.4670.0880.1420.2190.0890.2821.0000.3570.1840.2530.4020.1460.0950.3050.427-0.189
Na0.1400.3090.7740.8380.3970.1490.6300.2450.5310.3571.0000.2000.4070.5350.1720.1390.5010.5230.117
PO4-0.0900.1910.1660.2060.0800.0630.181-0.0360.0730.1840.2001.0000.0850.1210.2270.1050.1770.146-0.073
QUALITY0.1960.3550.4150.5100.1680.2150.4020.1210.4280.2530.4070.0851.0000.3150.3600.1090.3900.4860.264
SO4-0.0410.3960.5700.6290.2500.1520.3240.1810.4840.4020.5350.1210.3151.0000.1730.0240.3690.530-0.058
STATE0.2640.1860.1610.2100.1460.3920.1890.1100.1760.1460.1720.2270.3600.1731.0000.3980.3600.1840.309
SiO2-0.1660.2420.1350.1840.1170.1400.231-0.0200.1180.0950.1390.1050.1090.0240.3981.0000.1230.207-0.038
TDS0.0110.3410.4660.6040.1580.2770.4620.0870.4270.3050.5010.1770.3900.3690.3600.1231.0000.485-0.021
TH-0.0640.7800.6630.8390.2320.4820.7130.1370.8100.4270.5230.1460.4860.5300.1840.2070.4851.000-0.094
pH0.577-0.247-0.0620.0240.1030.1930.0310.1010.069-0.1890.117-0.0730.264-0.0580.309-0.038-0.021-0.0941.000

Missing values

2024-11-05T11:33:06.308099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T11:33:06.489308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

STATEDISTRICTpHECCO3HCO3ClSO4NO3PO4THCaMgNaKFSiO2TDSHARDNESSQUALITY
0A&N IslandsSouth Andaman7.74349.00.0183.025.01.01.00.0175.034.022.010.00.00.8424.0206.0HardSafe
1A&N IslandsSouth Andaman7.53660.00.0262.082.05.01.00.0250.052.029.051.01.00.2824.0381.0HardSafe
2A&N IslandsSouth Andaman7.50270.00.0116.035.01.00.00.0125.04.028.010.01.00.4624.0151.0HardSafe
3A&N IslandsSouth Andaman7.60311.00.0159.039.02.00.00.0135.026.017.015.02.00.3724.0198.0HardSafe
4A&N IslandsSouth Andaman7.822501.00.0512.0560.056.05.00.0465.056.079.0336.018.00.9524.01424.0HardModerately Safe
5A&N IslandsSouth Andaman7.71264.00.0153.035.01.01.00.0130.026.016.015.00.00.5724.0188.0HardSafe
6A&N IslandsSouth Andaman7.79535.00.0323.028.01.01.00.0245.038.036.019.00.00.4124.0321.0HardSafe
7A&N IslandsSouth Andaman7.78440.00.0226.039.01.01.00.0190.036.024.022.02.00.6924.0264.0HardSafe
8A&N IslandsN&M Andaman7.92592.00.0317.035.01.00.00.0265.034.044.015.02.00.3224.0325.0HardSafe
9A&N IslandsN&M Andaman7.78456.00.0226.021.05.02.00.0205.038.027.014.07.00.6024.0251.0HardSafe
STATEDISTRICTpHECCO3HCO3ClSO4NO3PO4THCaMgNaKFSiO2TDSHARDNESSQUALITY
8065West BengalSouth 24 Parganas8.871037.045.0305.074.00.00.00.0200.052.017.0102.03.50.3524.0462.0HardModerately Safe
8066West BengalSouth 24 Parganas8.931799.051.0348.0312.042.019.00.0300.034.052.0306.010.50.2624.01018.0HardModerately Safe
8067West BengalSouth 24 Parganas8.871812.039.0293.0379.05.00.00.0270.090.011.0297.07.80.7024.0987.0HardModerately Safe
8068West BengalSouth 24 Parganas8.54822.030.0275.0124.020.06.00.0160.014.030.0165.04.00.0024.0549.0HardModerately Safe
8069West BengalSouth 24 Parganas8.141231.00.0592.0102.03.00.00.065.024.01.0278.05.70.1124.0772.0ModeratelyModerately Safe
8070West BengalSouth 24 Parganas8.53827.027.0256.0117.032.06.00.0235.026.041.0122.04.20.0024.0521.0HardModerately Safe
8071West BengalSouth 24 Parganas8.48755.030.0275.099.00.00.00.0175.024.028.0120.03.80.0024.0460.0HardUnsafe
8072West BengalSouth 24 Parganas8.48856.030.0262.0131.00.00.00.0170.014.033.0135.04.60.0024.0496.0HardUnsafe
8073West BengalSouth 24 Parganas8.71717.030.0275.064.00.00.00.0285.056.035.032.05.20.1224.0378.0HardModerately Safe
8074West BengalSouth 24 Parganas8.741173.030.0250.0188.05.00.00.0265.030.046.0132.05.40.1424.0572.0HardModerately Safe

Duplicate rows

Most frequently occurring

STATEDISTRICTpHECCO3HCO3ClSO4NO3PO4THCaMgNaKFSiO2TDSHARDNESSQUALITY# duplicates
0GujaratRajkot7.921333.00.0537.0170.035.022.00.0180.036.022.0240.00.50.5549.0893.0HardModerately Safe2